The main data used in this tutorial and in the lecture are about the geolocalisation of french restaurants in Paris and in a department called Haute-Garonne. We use two different sources:
SIRENE has the advantages of being rigorous and exhaustive on the French territory.
OSM has many benefits, ensuring transparent data provenance and ownership, enabling real-time evolution of the database and, by allowing anyone to contribute, encouraging democratic decision making and citizen science.
sf::st_read function also work? Why?Use the readRDS function.
st_read would not work because ‘iris_31.rds’ is not a shapefile but a file already R formatted. Simply load it with the readRDS function.
Display the basemap of department 31 with plot(iris_31). What do you notice ?
We notice that R performs 3 graphs: one graph per variable in the sf object.
What is the functionality of the sf::st_geometry function? What solution do you propose then?
sf::st_geometry makes it possible to isolate the information contained in the ‘geometry’ column of the sf object. Using it, we put aside other variables (here CODE_IRIS, P14_POP and INSEE_COM).
In which projection is the map layer? Map it with another projection.
Test the Azimuthal Equidistant projection with “crs="+proj=aeqd +lat_0=90 +lon_0=0” to see a clear difference and create a layer called ‘iris_31_aeqd’.
Use the sf::st_crs function to guess the projection and sf::st_transform to change the projection.
#?st_crs
st_crs(iris_31)
par(mar = c(0,0,0,0), mfrow = c(1,2))
plot(st_geometry(iris_31))
iris_31_aeqd <- st_transform(iris_31, crs="+proj=aeqd +lat_0=90 +lon_0=0")
plot(st_geometry(iris_31_aeqd))Calculate the distance matrix between the 5 first iris of department 31. Do you get the same distance matrix if you are working on a layer projected in another projection?
Use map layers called ‘iris_31’ and ‘iris_31_aeqd’.
Units: m
[,1] [,2] [,3] [,4] [,5]
[1,] 0.00 51153.341 56509.22 53137.481 13756.98
[2,] 51153.34 0.000 20957.21 3086.011 49828.91
[3,] 56509.22 20957.212 0.00 11077.630 61863.18
[4,] 53137.48 3086.011 11077.63 0.000 54345.46
[5,] 13756.98 49828.910 61863.18 54345.458 0.00
Units: m
[,1] [,2] [,3] [,4] [,5]
[1,] 0.00 57206.922 62306.32 59385.205 13798.28
[2,] 57206.92 0.000 20957.48 3204.855 55368.23
[3,] 62306.32 20957.481 0.00 11076.220 66744.65
[4,] 59385.20 3204.855 11076.22 0.000 59843.30
[5,] 13798.28 55368.225 66744.65 59843.302 0.00
[1] FALSE
No, the two matrices are different.
Using the layer called ‘iris_31’, create a new aggregated map layer called ‘com_31’ which corresponds to the municipalities of department 31. Also keep in this new layer the information on the population in each municipality.
The map layer called ‘iris_31’ contains the 5 digit codes of municipalities in its variable INSEE_COM and the 2014 population in its column P14_POP.
Use the classic functions of dplyr package: select, group_by et summarize. These functions also work with sf objects.
library(dplyr)
com_31 <- iris_31 %>%
select(INSEE_COM,P14_POP) %>%
group_by(INSEE_COM) %>%
summarize(P14_POP= sum(P14_POP)) %>%
st_cast("MULTIPOLYGON")
plot(st_geometry(com_31))Using the data contained in ‘sir_31’, add to this layer the number of restaurants per municipality.
The code of each municipality is not in the ‘sir_31’ database. To create it, you have to create a variable called INSEE_COM (5 digits) which concatenates the DEPET (2 digits) and COMET (3 digits) variables.
sir_31 <- readRDS("../data/sir_31.rds")
com_31 <- left_join(com_31,
sir_31 %>%
mutate(INSEE_COM=paste0(DEPET,COMET)) %>%
group_by(INSEE_COM) %>%
summarize(nb_of_rest= n()) %>%
st_set_geometry(NULL),
by=c("INSEE_COM"="INSEE_COM")) %>%
mutate(nb_of_rest=ifelse(is.na(nb_of_rest),0,nb_of_rest))Aggregate all the information present in ‘com_31’ at the level of french intercommunalites (called EPCI) and call this new layer ‘epci_31’.
You have to use the database ‘table_MAUP.rds’ to have a match between the municipality code (CODGEO) and intercommunality code (EPCI).
table_MAUP <- readRDS("../data/table_MAUP.rds") %>%
select(CODGEO,EPCI)
epci_31 <- com_31 %>%
left_join(table_MAUP,by=c("INSEE_COM"="CODGEO")) %>%
group_by(EPCI) %>%
summarize(P14_POP=sum(P14_POP),nb_of_rest= sum(nb_of_rest)) %>%
st_cast("MULTIPOLYGON")
plot(st_geometry(epci_31))Using the cartography package, simply plot a map of french intercommunality with a proportional circle layer related to the number of restaurants.
The propSymbolsLayer function allows you to draw proportional circles.
library(cartography)
plot(st_geometry(epci_31), col = "ivory1", border = "ivory3",lwd =0.5,bg = "#FBEDDA")
propSymbolsLayer(epci_31, var = "nb_of_rest", inches = 0.2)We would like here to design EPCI maps that combine the number of restaurants and the number of restaurants per 10,000 inhabitants.
Data preparation:
For the creation of ‘bks’ et ‘cols’, use the getBreaks et carto.pal functions of the cartography package. For the creation of the typo variable, you can use the cut function and apply the parameters digit.lab = 2 and include.lowest = TRUE.
library(sf)
library(cartography)
library(dplyr)
# Import data
fra <- st_read("../data/fra.shp", quiet = TRUE)
epci_31 <- readRDS("../data/epci_31.rds")
# Create the variable
epci_31$nb_rest_10000inhab <- 10000 * epci_31$nb_of_rest / epci_31$P14_POP
# Define breaks
bks <- getBreaks(v = epci_31$nb_rest_10000inhab, method = "quantile", nclass = 4)
# Define color palette
cols <- carto.pal("orange.pal", length(bks)-1)
# Create a "typo"" variable
epci_31 <- epci_31 %>%
mutate(typo = cut(nb_rest_10000inhab,breaks = bks, dig.lab = 2,
include.lowest = TRUE))With the help of cartography package, make the following map which contains in a choropleth layer the variable nb_rest_10000inhab and in a proportional circle layer the variable nb_of_rest. Do the same thing with the ggplot2 package.
With cartography:
# Define plot margins
par(mar = c(0.2, 0.2, 1.4, 0.2), bg = "azure")
# Find EPCI bounding box
bb <- st_bbox(epci_31)
# Plot France using EPCI boundingbox
plot(st_geometry(fra), col="ivory", border = "ivory3",
xlim = bb[c(1, 3)], ylim = bb[c(2, 4)])
# Plot the choropleth layer
choroLayer(epci_31, var = "nb_rest_10000inhab",
breaks = bks, col = cols, border = "grey80", lwd = 0.5,
legend.pos = "topleft",add = TRUE,
legend.title.txt = "Number of restaurants\nfor 10,000 inhabitants")
# Plot proportionnal symbols
propSymbolsLayer(epci_31, var="nb_of_rest", col="#440170",border=NA,
legend.pos="left", inches=0.4, add = TRUE,
legend.title.txt = "Number of restaurants")
# Add a layout layer
layoutLayer(title = "Restaurants", sources = "Insee, 2018",
author = "Kim & Tim, 2018",
theme = "green.pal", col = "darkred",
coltitle = "white", postitle = "center",
frame = TRUE, scale = 10)
# Add a north (south) arrow
north(pos = "topright", south = TRUE)With ggplot2:
library(ggplot2)
map_ggplot <- ggplot() +
geom_sf(data = fra, colour = "ivory3",
fill = "ivory") +
geom_sf(data = epci_31, aes(fill = typo), colour = "grey80") +
scale_fill_manual(name = "Number of restaurants\nfor 10,000 inhabitants",
values = cols, na.value = "#303030")+
geom_sf(data = epci_31 %>% st_centroid(),
aes(size= nb_of_rest), color = "#440154CC", show.legend = 'point')+
scale_size(name = "Number of restaurants",
breaks = c(1, 500, 3200),
range = c(0,18))+
coord_sf(crs = 2154, datum = NA,
xlim = st_bbox(epci_31)[c(1,3)],
ylim = st_bbox(epci_31)[c(2,4)]
) +
theme_minimal() +
theme(panel.background = element_rect(fill = "azure",color=NA)) +
labs(
title = "Restaurants",
caption = "Insee, 2018\nKim & Tim, 2018"
)
plot(map_ggplot)What other solution could we use to display these two variable on the same map? Try it using the cartography package.
The propSymbolsChoroLayer function allows you to draw colored proportional circles.
# Define plot margins
par(mar = c(0.2, 0.2, 1.4, 0.2), bg = "azure")
# Find EPCI bounding box
bb <- st_bbox(epci_31)
# Plot France using EPCI boundingbox
plot(st_geometry(fra), col="ivory", border = "ivory3",
xlim = bb[c(1, 3)], ylim = bb[c(2, 4)])
# Plot EPCI
plot(st_geometry(epci_31), col="ivory3", border = "ivory2", add=T)
# Plot the choropleth layer
propSymbolsChoroLayer(epci_31, var = "nb_of_rest", var2 = "nb_rest_10000inhab",
breaks = bks, col = cols, border = "grey80", lwd = 0.5,
legend.var.pos = "topleft", legend.var2.pos = "left",
add = TRUE, inches = 0.4,
legend.var.title.txt = "Number of restaurants",
legend.var2.title.txt = "Number of restaurants\nfor 10,000 inhabitants")
# Add a layout layer
layoutLayer(title = "Restaurants", sources = "Insee, 2018",
author = "Kim & Tim, 2018",
theme = "green.pal", col = "darkred",
coltitle = "white", postitle = "center",
frame = TRUE, scale = 10)
# Add a north (south) arrow
north(pos = "topright", south = TRUE)Using the cartography package, display the number of restaurants and the number of restaurants per 10,000 inhabitants at the municipalities and EPCI scales. The two maps displayed side by side should be as much comparable as possible.
library(sf)
library(cartography)
library(dplyr)
# Import data
fra <- st_read("../data/fra.shp", quiet = TRUE)
epci_31 <- readRDS("../data/epci_31.rds")
com_31 <- readRDS("../data/com_31.rds")
# Create the variable
epci_31$nb_rest_10000inhab <- 10000 * epci_31$nb_of_rest / epci_31$P14_POP
com_31$nb_rest_10000inhab <- 10000 * com_31$nb_of_rest / com_31$P14_POP
# Define breaks for municipalities (we will use the same breaks for both maps)
bks_com <- getBreaks(v = com_31$nb_rest_10000inhab[com_31$nb_of_rest>0],
method = "quantile", nclass = 6)
# Define color palette
cols <- carto.pal("wine.pal", length(bks_com)-1)
# Define plot margins
par(mar = c(0, 0.1, 1.2, 0.1), bg = "azure", mfrow = c(1,2))
# Find EPCI bounding box
bb <- st_bbox(epci_31)
# Plot France using EPCI boundingbox
plot(st_geometry(fra), col="ivory", border = "ivory3",
xlim = bb[c(1, 3)], ylim = bb[c(2, 4)])
# Plot EPCI
plot(st_geometry(epci_31), col="ivory3", border = "ivory2", add=T)
# Plot the choropleth layer
propSymbolsChoroLayer(epci_31, var = "nb_of_rest", var2 = "nb_rest_10000inhab",
breaks = bks_com, col = cols, border = "ivory3",lwd = 0.6,
legend.var.pos = "bottomright", legend.var2.pos = "n",
add = TRUE, inches = 0.5,
legend.var.title.txt = "Number of restaurants")
# Add a layout layer
layoutLayer(title = "Restaurants", sources = "Insee, 2018",
author = "Kim & Tim, 2018",
theme = "green.pal", col = "darkred",
coltitle = "white", postitle = "center",
frame = FALSE, scale = NULL)
bb <- st_bbox(epci_31)
# Plot France using EPCI boundingbox
plot(st_geometry(fra), col="ivory", border = "ivory3",
xlim = bb[c(1, 3)], ylim = bb[c(2, 4)])
# Plot EPCI
plot(st_geometry(com_31), col="ivory3", border = "ivory2",lwd = .5, add=T)
# Plot the choropleth layer
propSymbolsChoroLayer(com_31, var = "nb_of_rest", var2 = "nb_rest_10000inhab",
breaks = bks_com, col = cols, border = "ivory3",lwd = 0.6,
fixmax = max(epci_31$nb_of_rest),
legend.var.pos = "n", legend.var2.pos = "bottom",
add = TRUE, inches = 0.5, legend.var2.values.rnd = 0,
legend.var2.title.txt = "Number of restaurants\nfor 10,000 inhabitants")
# Add a layout layer
layoutLayer(title = "Restaurants", sources = "",
author = "",
theme = "green.pal", col = "darkred",
coltitle = "white", postitle = "center",
frame = FALSE, scale = 10)
# Add a north (south) arrow
north(pos = "topright", south = TRUE)Load the dataset ‘sir_31’ used previously and map the more than 4,000 restaurants of department 31 with the mapview package. Try using different parameters to customize your map.
For example, you can use the map.types, col.regions, label, color, legend, layer.name, homebutton, lwd … parameters of the mapview function.
library(mapview)
library(sf)
library(cartography)
sir_31 <- readRDS("../data/sir_31.rds")
mapview(sir_31, map.types = "OpenStreetMap",
col.regions = "#940000",
label = paste(sir_31$L2_NORMALISEE, sir_31$NOMEN_LONG, sep = " - "),
color = "white", legend = TRUE, layer.name = "Restaurants in SIRENE",
homebutton = FALSE, lwd = 0.5)